1 citations · 1 across the 14 of their papers we have counts for
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V"Mean"ba: Visual State Space Models only need 1 hidden dimension
Tien-Yu Chi, Hung-Yueh Chiang, Chi-Chih Chang +2
Vision transformers dominate image processing tasks due to their superior performance. However, the quadratic complexity of self-attention limits the scalability of these systems a…
Quamba: A Post-Training Quantization Recipe for Selective State Space Models
Hung-Yueh Chiang, Chi-Chih Chang, Natalia Frumkin +2
State Space Models (SSMs) have emerged as an appealing alternative to Transformers for large language models, achieving state-of-the-art accuracy with constant memory complexity wh…
ELSA: Exploiting Layer-wise N:M Sparsity for Vision Transformer Acceleration
Ning-Chi Huang, Chi-Chih Chang, Wei-Cheng Lin +3
sparsity is an emerging model compression method supported by more and more accelerators to speed up sparse matrix multiplication in deep neural networks. Most existing $N{…
Palu: Compressing KV-Cache with Low-Rank Projection
Chi-Chih Chang, Wei-Cheng Lin, Chien-Yu Lin +7
Post-training KV-Cache compression methods typically either sample a subset of effectual tokens or quantize the data into lower numerical bit width. However, these methods cannot e…